Krembil Brain Institute, University Health Network (UHN), Toronto, Ontario, Canada; Institute of Medical Science, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada.
Krembil Brain Institute, University Health Network (UHN), Toronto, Ontario, Canada; Brain Health Imaging Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Institute of Medical Science, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada; Edmond J. Safra Parkinson Disease Program & Morton and Gloria Shulman Movement Disorder Unit, Neurology Division, Toronto Western Hospital, UHN, Toronto, Ontario, Canada.
Parkinsonism Relat Disord. 2024 Sep;126:106986. doi: 10.1016/j.parkreldis.2024.106986. Epub 2024 May 3.
Parkinson's disease is a neurodegenerative movement disorder associated with motor and non-motor symptoms causing severe disability as the disease progresses. The development of biomarkers for Parkinson's disease to diagnose patients earlier and predict disease progression is imperative. As artificial intelligence and machine learning techniques efficiently process data and can handle multiple data types, we reviewed the literature to determine the extent to which these techniques have been applied to biomarkers for Parkinson's disease and movement disorders. We determined that the most applicable machine learning techniques are support vector machines and neural networks, depending on the size and type of the data being analyzed. Additionally, more complex machine learning techniques showed increased accuracy when compared to less complex techniques, especially when multiple machine learning models were combined. We can conclude that artificial intelligence and machine learning techniques may have the capacity to significantly boost diagnostic capacity in movement disorders and Parkinson's disease.
帕金森病是一种与运动和非运动症状相关的神经退行性运动障碍，随着疾病的进展会导致严重的残疾。开发帕金森病生物标志物以更早地诊断患者并预测疾病进展至关重要。由于人工智能和机器学习技术能够高效地处理数据并处理多种数据类型，我们查阅了文献，以确定这些技术在多大程度上已应用于帕金森病和运动障碍的生物标志物。我们确定，最适用的机器学习技术是支持向量机和神经网络，具体取决于正在分析的数据的大小和类型。此外，与较简单的技术相比，更复杂的机器学习技术显示出更高的准确性，尤其是当结合使用多个机器学习模型时。我们可以得出结论，人工智能和机器学习技术可能有能力极大地提高运动障碍和帕金森病的诊断能力。
